Adib A, Samandizadeh M A. COMPARISON ABILITY OF GA AND DP METHODS FOR OPTIMIZATION OF RELEASED WATER FROM RESERVOIR DAM BASED ON PRODUCED DIFFERENT SCENARIOS BY MARKOV CHAIN METHOD. International Journal of Optimization in Civil Engineering. 2016; 6 (1) :43-62URL: http://ijoce.iust.ac.ir/article-1-237-fa.html

COMPARISON ABILITY OF GA AND DP METHODS FOR OPTIMIZATION OF RELEASED WATER FROM RESERVOIR DAM BASED ON PRODUCED DIFFERENT SCENARIOS BY MARKOV CHAIN METHOD. دانشگاه علم و صنعت ایران. 1394; 6 (1) :43-62

Planning for supply water demands (drinkable and irrigation water demands) is a necessary problem. For this purpose, three subjects must be considered (optimization of water supply systems such as volume of reservoir dams, optimization of released water from reservoir and prediction of next droughts). For optimization of volume of reservoir dams, yield model is applied. Reliability of yield model is more than perfect model and cost of solution of this model is less than other methods. For optimization of released water from reservoir dams, different methods can be applied. In this research, dynamic programming method (a discrete method for optimization) and genetic algorithm (a searcher method for optimization) are considered for optimization of released water from the Karaj reservoir dam. The Karaj dam locates in west of Tehran. This research shows that reliability and resiliency of GA method is more than DP method and vulnerability of GA method is less than DP method. For improving of results of GA method, mutation rate of GA method is considered from 0.005 to 0.3 for different generations. For prediction extreme droughts in future, the Markov chain method is used. Based on generated data by Markov chain method, optimum volume of reservoir dam is determined by yield model. Then optimum released water from reservoir dam is determined by DP and GA methods for different scenarios that produced by Markov chain method. The Markov chain and yield model show that volume of reservoir Karaj dam should increase 123 MCM for overcoming to next droughts.